---------------------------------------------------------------------***--------------------------------------------------------------------Abstract In this work a multilingual speaker identification system is proposed. The feature extraction techniques employed in system extract Mel frequency cepstral coefficient (MFCC), delta mel frequency cepstral coefficient (DMFCC) and format frequency. Th...

In this paper we introduce a robust feature extractor, dubbed as Modified Function Cepstral Coefficients (MODFCC), based on gammachirp filterbank, Relative Spectral (RASTA) and Autoregressive Moving-Average (ARMA) filter. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. In speech recognition systems Mel-Fr...

The paper describes a speaker independent segmentation system for breaking Arabic uttered sentences into its constituent syllables. The goal is to construct a database of acoustical Arabic syllables as a step towards a syllable-based Arabic speech verification/recognition system. The proposed technique segments the utterances based on maxima extraction from delta function of 1st MFC coefficient...

The paper present effective method for recognition of digit, numbers. Most of speech recognition systems contain two main modules as follow “feature extraction” and “feature matching”. In this project, (MFCC) Mel Frequency Cepstrum coefficient algorithm is used to simulate feature extraction module. Using this algorithm, the Cepstral Coefficients are calculated on Mel frequency scale. VQ (vecto...

Performance of an automatic speech recognition (ASR) system tends to be dramatically degraded in the presence of impulsive noise. In the previous work [1], we proposed flooring the observation probability (FOP) to compensate the adverse effect of impulsive noise on sensitive dimensions of Mel-frequency cepstral coefficient (MFCC) features. Linear prediction cepstral coefficient (LPCC) is anothe...

Based on research conducted by the Institute of Qur'anic Sciences (IIQ) as many 65% Muslims in Indonesia are illiterate Qur'an. In previous studies, was detection Arabic word pronunciation errors against non-natives using Mel Frequency Cepstral Coefficient (MFCC) and Support Vector Machine (SVM) methods with a test result 54.6%. Due to low accuracy results this study aims design build system th...

Enhancing the performance of emotional speaker recognition process has witnessed an increasing interest in the last years. This paper highlights a methodology for speaker recognition under different emotional states based on the multiclass Support Vector Machine (SVM) classifier. We compare two feature extraction methods which are used to represent emotional speech utterances in order to obtain...

----------------------------------------------------------------------ABSTRACT------------------------------------------------------------------The objective approach has an advantage over the manual, which provides consistence measurement required for assessment of stuttered speech. The number of dimensions (multi dimension) plays a key role in objective assessment of stuttering. The purpose o...

Speakers are generally identified by using features derived from the Fourier transform magnitude. The Modified group delay feature(MODGDF) derived from the Fourier transform phase has been used effectively for speaker recognition in our previous efforts.Although the efficacy of the MODGDF as an alternative to the MFCC is yet to be established, it has been shown in our earlier work that composit...

The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, gets degraded with environmental noises, channel variation, physical behavioral changes in speaker. types Speaker related feature play crucial role improving systems. Gammatone Frequency Cepstral Coefficient (GFCC) features has been widely used to develop robust conventional machine ...